Master class: Biodiversity_Data_from_Field_to_Yield

Date & time: Tuesday, October 2 – Saturday, October 6, 2018 (5 full days)

Location: Universidade Federal do Rio Grande do Norte, Laboratório de Modelagem do departamento e Ecologia/DECOL, First floor, Natal, Brazil

Teachers: Alexandre Antonelli & Alexander Zizka, University of Gothenburg, Gothenburg Global Biodiversity Centre, and German Centre for Integrative Biodiversity Research

http://antonelli-lab.net

http://ggbc.gu.se

https://www.idiv.de/

Detailed schedule and locations

Day 1 (Oct, 2nd): 8:00 - 18:00 (local: UFRN/ Laboratório de Modelagem do departamento e Ecologia/DECOL - 1° andar)

Day 2 (Oct, 3rd): 5 am (local: Canyon dos Apertados. Meeting point: gas station Petrobrás “Posto 1002” - Av Roberto Freire, 2971 - Capim Macio)

Day 3 (Oct, 4th): 7 am (local: Parque das Dunas. Meeting point: DECOL)

Day 4 (Oct, 5th): 8 - 18 (local: UFRN/ Laboratório de Modelagem do DECOL)

Day 5 (oct, 6th): 8 - 18 (local: UFRN/ Laboratório de Modelagem do DECOL)

Before the course

Please:

Please do not hesitate to contact us if you have any questions.

Installation

Please run the following code (just copy it into the R console) before the course to make sure all dependencies are installed and we can start smoothly.

install.packages(c("countrycode", "ConR", "devtools", "ggmap", "mapproj", "maps",
                   "rgbif", "raster", "rnaturalearth", "sp", "tidyverse",
                   "viridis"))

library(devtools)

install_github(repo = "azizka/speciesgeocodeR")
install_github(repo = "azizka/sampbias")
install_github(repo = "azizka/CoordinateCleaner")

Objectives

  1. Exemplify the various uses of biodiversity data for exploratory and question-driven research in ecology, evolution and biogeography

  2. Familiarize participants with new bioinformatic tools for handling and processing ‘big data’, including dealing with data errors and biases

  3. Provide the participants with a workflow to use large scale species occurrence data for biodiversity analyses (including point localities and range maps)

  4. Provide an overview of how to use R for analysing large datasets of species occurrences (including data mining, visualization, exploration, cleaning and applications)

Background

The public availability of large-scale species distribution data has increased drastically over the last ten years. In particular, the digitalization of collections from museums and herbaria, the input from human and machine observations, and the aggregation of information in public databases such as the Global Biodiversity Information Facility (GBIF) have contributed significantly to this development. This is leading to a ‘big data’ revolution in biogeography, which holds an enormous but still poorly explored potential for understanding large scale patterns and drivers of biodiversity.

Project Assignment

During the course you will pick a taxonomic group of your interest and then independently collect occurrence data and answer biogeographic questions on this group with guidance from the teachers.

Software exercises and Project questions

After collecting occurrence records in the field we will spend day four of the course getting to know different software to process the data and answer the research questions of your projects. There are seven exercises, each one representing a step to gather and process geographic occurrence information to tackle questions of your research project.

Most of the exercises are in R, although exercise 1) and 6) can also be done using a web browser. Each exercise comes as a set of questions with some suggestions on how to solve them. If necessary, example answers are available to guide the students. Exercises 1) and 2) are compulsory, beyond that you can chose which exercises to follow based on the questions of your project.

Examination

Grades are pass/fail. Successful participants should participate in all course days and present a project on the last day. A certification will be issued for all participants.

Literature

  1. Meyer et al. (2016) Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecology Letters 19:992-1006.

  2. Schmidt et al. (2017) Diversity, distribution and preliminary conservation status of the flora of Burkina Faso. Magnolia Press.

  3. Antonelli et al. (2018) Amazonia is the primary source of Neotropical biodiversity. PNAS 6pp

  4. One of the following suggestions (depending on your own interests):